1. Assuming you hypothesized positive autocorrelation (i.e., like goes with
like and Geary coefficient SMALLER than 1), then you should regard the
proportion as small as your p-value.

2. Your comparison of geary with qapping adjacency against difference matrix
is sensible, though I think Geary uses squared differences.

3. I'm sure you realize (but is worth confirming) that the two p-values
being exactly identical (.008) is something of a coincidence, in the sense
that these are constructed by generating 'random' distributions on the fly.
So even within one method, such as qap, you can get a different p-value the
next time you run it. The variation decreases with the number of
permutations used to construct the distribution.

> Dear SocNet,
> I received two different results using first QAP (matrix x matrix
> correlation)
> and then Autocorrelation (matrix x vector -- interval/ratio data --
> correlation). The same data was used for both tests. Please read on:
> 1. Autocorrelation:
> I used the normalized degree centrality score (vector data), with a
> square matrix.
> I performed an autocorrelation on this data using Geary's C statistic
> as the default model,
> I got the following results:
> Autocorrelation: 0.629
> P as Large : 0.992
> P as Small: 0.008
> According to my understanding of the Geary statistic (just based on the
> UCINET help),
> this means I don't have a significant correlation by a long shot.
> 2. QAP:
>
> However, when I took that same normalized degree score, transformed it
> into a square matrix
> through the UCINET command Data --> Attribute (chose absolute
> difference as method), and
> then ran a QAP with the same square matrix data, I got significant
> results:
>
> Pearson Correlation: r = 0.058 p = 0.008
> Notice that P as Small for Geary is the same p value for Pearson.
> However, my understanding
> re: the Geary is that one should read the P as Large value.
> So what's up? Thanks in advance, Christina
>